In [2]:
import pandas as pd #pandas (pd): Data manipulation ke liye
import plotly.express as px #plotly.express (px): Data visualization library for easy aur quick plots
import plotly.graph_objects as go #plotly.graph_objects (go): Advanced and customizable graphs banane ke liye
import plotly.io as pio #plotly.io (pio): Graph templates ko customize karne ke liye
import plotly.colors as colors
pio.templates.default = "plotly_white"
In [3]:
data = pd.read_csv("Sample - Superstore.csv", encoding='latin-1')
#encoding='latin-1': Special characters ko properly read karne ke liye encoding use hui hai
data.head()
Out[3]:
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Postal Code | Region | Product ID | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 11/8/2016 | 11/11/2016 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-BO-10001798 | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 |
| 1 | 2 | CA-2016-152156 | 11/8/2016 | 11/11/2016 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-CH-10000454 | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 |
| 2 | 3 | CA-2016-138688 | 6/12/2016 | 6/16/2016 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | 90036 | West | OFF-LA-10000240 | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 |
| 3 | 4 | US-2015-108966 | 10/11/2015 | 10/18/2015 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | FUR-TA-10000577 | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 |
| 4 | 5 | US-2015-108966 | 10/11/2015 | 10/18/2015 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | OFF-ST-10000760 | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 |
5 rows × 21 columns
Descriptive statistics of the dataset¶
In [4]:
data.describe()
Out[4]:
| Row ID | Postal Code | Sales | Quantity | Discount | Profit | |
|---|---|---|---|---|---|---|
| count | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 |
| mean | 4997.500000 | 55190.379428 | 229.858001 | 3.789574 | 0.156203 | 28.656896 |
| std | 2885.163629 | 32063.693350 | 623.245101 | 2.225110 | 0.206452 | 234.260108 |
| min | 1.000000 | 1040.000000 | 0.444000 | 1.000000 | 0.000000 | -6599.978000 |
| 25% | 2499.250000 | 23223.000000 | 17.280000 | 2.000000 | 0.000000 | 1.728750 |
| 50% | 4997.500000 | 56430.500000 | 54.490000 | 3.000000 | 0.200000 | 8.666500 |
| 75% | 7495.750000 | 90008.000000 | 209.940000 | 5.000000 | 0.200000 | 29.364000 |
| max | 9994.000000 | 99301.000000 | 22638.480000 | 14.000000 | 0.800000 | 8399.976000 |
In [6]:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9994 entries, 0 to 9993 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Row ID 9994 non-null int64 1 Order ID 9994 non-null object 2 Order Date 9994 non-null object 3 Ship Date 9994 non-null object 4 Ship Mode 9994 non-null object 5 Customer ID 9994 non-null object 6 Customer Name 9994 non-null object 7 Segment 9994 non-null object 8 Country 9994 non-null object 9 City 9994 non-null object 10 State 9994 non-null object 11 Postal Code 9994 non-null int64 12 Region 9994 non-null object 13 Product ID 9994 non-null object 14 Category 9994 non-null object 15 Sub-Category 9994 non-null object 16 Product Name 9994 non-null object 17 Sales 9994 non-null float64 18 Quantity 9994 non-null int64 19 Discount 9994 non-null float64 20 Profit 9994 non-null float64 dtypes: float64(3), int64(3), object(15) memory usage: 1.6+ MB
Converting Date Columns¶
In [7]:
data['Order Date'] = pd.to_datetime(data['Order Date'])
data['Ship Date'] = pd.to_datetime(data['Ship Date'])
#Date Conversion: Order Date aur Ship Date columns ko datetime format me convert kiya gaya hai for date-based analysis.
In [8]:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9994 entries, 0 to 9993 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Row ID 9994 non-null int64 1 Order ID 9994 non-null object 2 Order Date 9994 non-null datetime64[ns] 3 Ship Date 9994 non-null datetime64[ns] 4 Ship Mode 9994 non-null object 5 Customer ID 9994 non-null object 6 Customer Name 9994 non-null object 7 Segment 9994 non-null object 8 Country 9994 non-null object 9 City 9994 non-null object 10 State 9994 non-null object 11 Postal Code 9994 non-null int64 12 Region 9994 non-null object 13 Product ID 9994 non-null object 14 Category 9994 non-null object 15 Sub-Category 9994 non-null object 16 Product Name 9994 non-null object 17 Sales 9994 non-null float64 18 Quantity 9994 non-null int64 19 Discount 9994 non-null float64 20 Profit 9994 non-null float64 dtypes: datetime64[ns](2), float64(3), int64(3), object(13) memory usage: 1.6+ MB
In [9]:
data.head()
Out[9]:
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Postal Code | Region | Product ID | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-BO-10001798 | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 |
| 1 | 2 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-CH-10000454 | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 |
| 2 | 3 | CA-2016-138688 | 2016-06-12 | 2016-06-16 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | 90036 | West | OFF-LA-10000240 | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 |
| 3 | 4 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | FUR-TA-10000577 | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 |
| 4 | 5 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | OFF-ST-10000760 | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 |
5 rows × 21 columns
Adding New Date-Based Columns¶
In [12]:
data['Order Month'] = data['Order Date'].dt.month
data['Order Year'] = data['Order Date'].dt.year
data['Order Day of Week'] = data['Order Date'].dt.dayofweek
#Order Month: Order date se month extract
#Order Year: Order date se year extract
#Order Day of Week: Week ka day (0 for Monday, 6 for Sunday)
In [13]:
data.head()
Out[13]:
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | Order Month | Order Year | Order Day of Week | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 | 11 | 2016 | 1 |
| 1 | 2 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 | 11 | 2016 | 1 |
| 2 | 3 | CA-2016-138688 | 2016-06-12 | 2016-06-16 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 | 6 | 2016 | 6 |
| 3 | 4 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 | 10 | 2015 | 6 |
| 4 | 5 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 | 10 | 2015 | 6 |
5 rows × 24 columns
Monthly Sales Analysis¶
In [14]:
sales_by_month = data.groupby('Order Month')['Sales'].sum().reset_index()
fig = px.line(sales_by_month,
x='Order Month',
y='Sales',
title='Monthly Sales Analysis')
fig.show()
#Data Grouping:
#data.groupby('Order Month')['Sales'].sum() se har month ki total sales .
#.reset_index() data ko structured format me rakhta hai.
#px.line: Monthly sales trend show karne ke liye line chart .
#fig.show(): Graph display karta hai.
Sales Analysis by Category¶
In [15]:
sales_by_category = data.groupby('Category')['Sales'].sum().reset_index()
In [16]:
sales_by_category
Out[16]:
| Category | Sales | |
|---|---|---|
| 0 | Furniture | 741999.7953 |
| 1 | Office Supplies | 719047.0320 |
| 2 | Technology | 836154.0330 |
In [17]:
fig = px.pie(sales_by_category,
values='Sales',
names='Category',
hole=0.5,
color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Sales Analysis by Category', title_font=dict(size=24))
fig.show()
#groupby('Category'): Category-wise sales .
#Pie Chart:
#px.pie: Sales proportions ko pie chart me show karta hai.
#hole=0.5: Donut-style chart banata hai.
#Pastel Colors: Chart me soft color palette use kiya gaya hai.
Sales Analysis by Sub-Category¶
In [18]:
sales_by_subcategory = data.groupby('Sub-Category')['Sales'].sum().reset_index()
fig = px.bar(sales_by_subcategory,
x='Sub-Category',
y='Sales',
title='Sales Analysis by Sub-Category')
fig.show()
Monthly Profit Analysis¶
In [19]:
profit_by_month = data.groupby('Order Month')['Profit'].sum().reset_index()
In [20]:
profit_by_month
Out[20]:
| Order Month | Profit | |
|---|---|---|
| 0 | 1 | 9134.4461 |
| 1 | 2 | 10294.6107 |
| 2 | 3 | 28594.6872 |
| 3 | 4 | 11587.4363 |
| 4 | 5 | 22411.3078 |
| 5 | 6 | 21285.7954 |
| 6 | 7 | 13832.6648 |
| 7 | 8 | 21776.9384 |
| 8 | 9 | 36857.4753 |
| 9 | 10 | 31784.0413 |
| 10 | 11 | 35468.4265 |
| 11 | 12 | 43369.1919 |
In [21]:
fig = px.line(profit_by_month,
x='Order Month',
y='Profit',
title='Monthly Profit Analysis')
fig.show()
Profit Analysis by Category¶
In [22]:
profit_by_category = data.groupby('Category')['Profit'].sum().reset_index()
In [23]:
profit_by_category
Out[23]:
| Category | Profit | |
|---|---|---|
| 0 | Furniture | 18451.2728 |
| 1 | Office Supplies | 122490.8008 |
| 2 | Technology | 145454.9481 |
In [24]:
fig = px.pie(profit_by_category,
values='Profit',
names='Category',
hole=0.5,
color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Profit Analysis by Category', title_font=dict(size=24))
fig.show()
Profit Analysis by Sub-Category¶
In [25]:
profit_by_subcategory = data.groupby('Sub-Category')['Profit'].sum().reset_index()
In [26]:
fig = px.bar(profit_by_subcategory, x='Sub-Category',
y='Profit',
title='Profit Analysis by Sub-Category')
fig.show()
In [27]:
data.head()
Out[27]:
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | Order Month | Order Year | Order Day of Week | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 | 11 | 2016 | 1 |
| 1 | 2 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 | 11 | 2016 | 1 |
| 2 | 3 | CA-2016-138688 | 2016-06-12 | 2016-06-16 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 | 6 | 2016 | 6 |
| 3 | 4 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 | 10 | 2015 | 6 |
| 4 | 5 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 | 10 | 2015 | 6 |
5 rows × 24 columns
Sales and Profit Analysis by Customer Segment¶
In [28]:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
color_palette = colors.qualitative.Pastel
fig = go.Figure()
fig.add_trace(go.Bar(x=sales_profit_by_segment['Segment'],
y=sales_profit_by_segment['Sales'],
name='Sales',
marker_color=color_palette[0]))
fig.add_trace(go.Bar(x=sales_profit_by_segment['Segment'],
y=sales_profit_by_segment['Profit'],
name='Profit',
marker_color=color_palette[1]))
fig.update_layout(title='Sales and Profit Analysis by Customer Segment',
xaxis_title='Customer Segment', yaxis_title='Amount')
fig.show()
Analyse sales-to-profit ratio¶
In [29]:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
sales_profit_by_segment['Sales_to_Profit_Ratio'] = sales_profit_by_segment['Sales'] / sales_profit_by_segment['Profit']
print(sales_profit_by_segment[['Segment', 'Sales_to_Profit_Ratio']])
Segment Sales_to_Profit_Ratio 0 Consumer 8.659471 1 Corporate 7.677245 2 Home Office 7.125416
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